The goal of cancer genomics is to determine the gene expression profile of normal, precancerous, and cancerous cells, with the hope of improving detection, diagnosis, and treatment for patients. The completion of the human genome project has accelerated this progression, and gene microarrays have set the stage for genomic pathology analysis. However, current technology to obtain RNA from specimens is inefficient and requires at least 2 million cells. This is especially problematic with tumors where little starting material is available, or where tumors are heterogeneous and require microdissection or similar methods that drastically limit the amount of starting material. To this end, invention of aRNA (amplified antisense) was a major step ahead but is not ideal for serial amplification or preparation for microarray analysis from a scant amount of sample. Based on the recently developed RNA-PCR Technology, we propose a format that can amplify mRNA from less than 100 cells. We postulate that further development and validation of this technology will lead to a routine procedure to obtain gene expression profiles (GEPs) from just a few or even single cells. In the R21 phase, we will use an NCI RNA standard and cultured sarcoma cell lines to compare amplified and non-amplified RNA with rigorous statistical analyses using Genetrix, a software tool developed here to analyze microarray data. Conditions will be optimized and worked out for pathological specimens. When the fidelity and efficacy of RNA-PCR amplification are validated, we will progress to the R33 phase to field test in cancer using childhood sarcoma as a model. We hope to achieve high resolution GEP using Laser Capture Micro-dissection microscopy to dissect histopathology sections. We will verify whether GEPs from a few cells are as predictive of biologic behavior as conventional GEPs, using the same clinical material used in Triche?s Director?s Challenge grant. We will also apply microarray analysis to skinny needle biopsies and cytological preparations, which were previously not possible. Bioinformatic analysis with Genetrix, which allows correlation of expression on a gene-by-gene basis with patients? clinical data (e.g., age, sex, pathological diagnosis, therapeutic protocol, etc.), may identify new markers of high predictive value that are obscured due to tissue heterogeneity, or unavailable due to limited amount of materials (e.g., those in tissue banks). This protocol is simple, fast, and reliable and uses minimal samples widely obtainable. The method can be applied to all cancers.